"""simple docstring"""



import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__:int									=			get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
   from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE__:Any									=			256047
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			256145






@require_sentencepiece
@require_tokenizers
class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					int							  =						NllbTokenizer
 _snake_case       :					Tuple							  =						NllbTokenizerFast
 _snake_case       :					Dict							  =						True
 _snake_case       :					List[Any]							  =						True
 _snake_case       :					int							  =						{}


 def 						a__   ( self						):
        super().setUp()

        # We have a SentencePiece fixture for testing
        __a       =    NllbTokenizer(lowerCamelCase    ,		keep_accents=lowerCamelCase						)
        tokenizer.save_pretrained(self.tmpdirname						)


 def 						a__   ( self						):
        __a       =    NllbTokenizer(lowerCamelCase    ,		keep_accents=lowerCamelCase						)

        __a       =    tokenizer.tokenize("This is a test"						)
        self.assertListEqual(lowerCamelCase    ,		["▁This", "▁is", "▁a", "▁t", "est"]						)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCamelCase						)    ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,		)

        __a       =    tokenizer.tokenize("I was born in 92000, and this is falsé."						)
        self.assertListEqual(
            lowerCamelCase    ,		[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ]    ,		)
        __a       =    tokenizer.convert_tokens_to_ids(lowerCamelCase						)
        self.assertListEqual(
            lowerCamelCase    ,		[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]    ,		)

        __a       =    tokenizer.convert_ids_to_tokens(lowerCamelCase						)
        self.assertListEqual(
            lowerCamelCase    ,		[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ]    ,		)


 def 						a__   ( self						):
        __a       =    (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
                      __a       =    self.rust_tokenizer_class.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
                      __a       =    self.tokenizer_class.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)

                      __a       =    tempfile.mkdtemp()

                      __a       =    tokenizer_r.save_pretrained(lowerCamelCase						)
                      __a       =    tokenizer_p.save_pretrained(lowerCamelCase						)

                      # Checks it save with the same files + the tokenizer.json file for the fast one
                      self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files						)						)
                      __a       =    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f						)
                      self.assertSequenceEqual(lowerCamelCase    ,		lowerCamelCase						)

                      # Checks everything loads correctly in the same way
                      __a       =    tokenizer_r.from_pretrained(lowerCamelCase						)
                      __a       =    tokenizer_p.from_pretrained(lowerCamelCase						)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowerCamelCase    ,		lowerCamelCase						)						)

                      shutil.rmtree(lowerCamelCase						)

                      # Save tokenizer rust, legacy_format=True
                      __a       =    tempfile.mkdtemp()

                      __a       =    tokenizer_r.save_pretrained(lowerCamelCase    ,		legacy_format=lowerCamelCase						)
                      __a       =    tokenizer_p.save_pretrained(lowerCamelCase						)

                      # Checks it save with the same files
                      self.assertSequenceEqual(lowerCamelCase    ,		lowerCamelCase						)

                      # Checks everything loads correctly in the same way
                      __a       =    tokenizer_r.from_pretrained(lowerCamelCase						)
                      __a       =    tokenizer_p.from_pretrained(lowerCamelCase						)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowerCamelCase    ,		lowerCamelCase						)						)

                      shutil.rmtree(lowerCamelCase						)

                      # Save tokenizer rust, legacy_format=False
                      __a       =    tempfile.mkdtemp()

                      __a       =    tokenizer_r.save_pretrained(lowerCamelCase    ,		legacy_format=lowerCamelCase						)
                      __a       =    tokenizer_p.save_pretrained(lowerCamelCase						)

                      # Checks it saved the tokenizer.json file
                      self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files						)						)

                      # Checks everything loads correctly in the same way
                      __a       =    tokenizer_r.from_pretrained(lowerCamelCase						)
                      __a       =    tokenizer_p.from_pretrained(lowerCamelCase						)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowerCamelCase    ,		lowerCamelCase						)						)

                      shutil.rmtree(lowerCamelCase						)


 @require_torch
 def 						a__   ( self						):
        if not self.test_seqaseq:
               return

        __a       =    self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"						):
                      # Longer text that will definitely require truncation.
                      __a       =    [
                          " UN Chief Says There Is No Military Solution in Syria",
                          " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                          " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                          " will only worsen the violence and misery for millions of people.",
                      ]
                      __a       =    [
                          "Şeful ONU declară că nu există o soluţie militară în Siria",
                          "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                          " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                          " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
                      ]
                      try:
                             __a       =    tokenizer.prepare_seqaseq_batch(
                                 src_texts=lowerCamelCase    ,		tgt_texts=lowerCamelCase    ,		max_length=3    ,		max_target_length=10    ,		return_tensors="pt"    ,		src_lang="eng_Latn"    ,		tgt_lang="ron_Latn"    ,		)
                      except NotImplementedError:
                             return
                      self.assertEqual(batch.input_ids.shape[1]    ,		3						)
                      self.assertEqual(batch.labels.shape[1]    ,		10						)
                      # max_target_length will default to max_length if not specified
                      __a       =    tokenizer.prepare_seqaseq_batch(
                          lowerCamelCase    ,		tgt_texts=lowerCamelCase    ,		max_length=3    ,		return_tensors="pt"						)
                      self.assertEqual(batch.input_ids.shape[1]    ,		3						)
                      self.assertEqual(batch.labels.shape[1]    ,		3						)

                      __a       =    tokenizer.prepare_seqaseq_batch(
                          src_texts=lowerCamelCase    ,		max_length=3    ,		max_target_length=10    ,		return_tensors="pt"						)
                      self.assertEqual(batch_encoder_only.input_ids.shape[1]    ,		3						)
                      self.assertEqual(batch_encoder_only.attention_mask.shape[1]    ,		3						)
                      self.assertNotIn("decoder_input_ids"    ,		lowerCamelCase						)


 @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."						)
 def 						a__   ( self						):
        pass






 def 						a__   ( self						):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
                      __a       =    [AddedToken("<special>"    ,		lstrip=lowerCamelCase						)]

                      __a       =    self.rust_tokenizer_class.from_pretrained(
                          lowerCamelCase    ,		additional_special_tokens=lowerCamelCase    ,		**lowerCamelCase						)
                      __a       =    tokenizer_r.encode("Hey this is a <special> token"						)

                      __a       =    tokenizer_r.encode("<special>"    ,		add_special_tokens=lowerCamelCase						)[0]

                      self.assertTrue(special_token_id in r_output						)

                      if self.test_slow_tokenizer:
                             __a       =    self.rust_tokenizer_class.from_pretrained(
                                 lowerCamelCase    ,		additional_special_tokens=lowerCamelCase    ,		**lowerCamelCase    ,		)
                             __a       =    self.tokenizer_class.from_pretrained(
                                 lowerCamelCase    ,		additional_special_tokens=lowerCamelCase    ,		**lowerCamelCase						)

                             __a       =    tokenizer_p.encode("Hey this is a <special> token"						)

                             __a       =    tokenizer_cr.encode("Hey this is a <special> token"						)

                             self.assertEqual(lowerCamelCase    ,		lowerCamelCase						)
                             self.assertEqual(lowerCamelCase    ,		lowerCamelCase						)
                             self.assertTrue(special_token_id in p_output						)
                             self.assertTrue(special_token_id in cr_output						)






@require_torch
@require_sentencepiece
@require_tokenizers
class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Tuple							  =						"""facebook/nllb-200-distilled-600M"""
 _snake_case       :					List[str]							  =						[
     """ UN Chief Says There Is No Military Solution in Syria""",
     """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
 ]
 _snake_case       :					str							  =						[
     """Şeful ONU declară că nu există o soluţie militară în Siria""",
     """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
     """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
     """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
 ]
 _snake_case       :					int							  =						[
     256_047,
     16_297,
     134_408,
     8_165,
     248_066,
     14_734,
     950,
     1_135,
     105_721,
     3_573,
     83,
     27_352,
     108,
     49_486,
     2,
 ]


 @classmethod
 def 						a__   ( cls						):
        __a       =    NllbTokenizer.from_pretrained(
            cls.checkpoint_name    ,		src_lang="eng_Latn"    ,		tgt_lang="ron_Latn"						)
        __a       =    1
        return cls


 def 						a__   ( self						):
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]    ,		256001						)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]    ,		256002						)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]    ,		256057						)


 def 						a__   ( self						):
        __a       =    self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
        self.assertListEqual(self.expected_src_tokens    ,		lowerCamelCase						)


 def 						a__   ( self						):
        self.assertIn(lowerCamelCase    ,		self.tokenizer.all_special_ids						)
        # fmt: off
        __a       =    [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
        # fmt: on

        __a       =    self.tokenizer.decode(lowerCamelCase    ,		skip_special_tokens=lowerCamelCase						)
        __a       =    self.tokenizer.decode(generated_ids[1:]    ,		skip_special_tokens=lowerCamelCase						)
        self.assertEqual(lowerCamelCase    ,		lowerCamelCase						)
        self.assertNotIn(self.tokenizer.eos_token    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    ["this is gunna be a long sentence " * 20]
        assert isinstance(src_text[0]    ,		lowerCamelCase						)
        __a       =    10
        __a       =    self.tokenizer(lowerCamelCase    ,		max_length=lowerCamelCase    ,		truncation=lowerCamelCase						).input_ids[0]
        self.assertEqual(ids[-1]    ,		2						)
        self.assertEqual(ids[0]    ,		lowerCamelCase						)
        self.assertEqual(len(lowerCamelCase						)    ,		lowerCamelCase						)


 def 						a__   ( self						):
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]						)    ,		[256203, 3]						)


 def 						a__   ( self						):
        __a       =    tempfile.mkdtemp()
        __a       =    self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(lowerCamelCase						)
        __a       =    NllbTokenizer.from_pretrained(lowerCamelCase						)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,		lowerCamelCase						)


 @require_torch
 def 						a__   ( self						):
        __a       =    self.tokenizer(
            self.src_text    ,		text_target=self.tgt_text    ,		padding=lowerCamelCase    ,		truncation=lowerCamelCase    ,		max_length=len(self.expected_src_tokens						)    ,		return_tensors="pt"    ,		)
        __a       =    shift_tokens_right(
            batch["labels"]    ,		self.tokenizer.pad_token_id    ,		self.tokenizer.lang_code_to_id["ron_Latn"]						)

        self.assertIsInstance(lowerCamelCase    ,		lowerCamelCase						)

        self.assertEqual((2, 15)    ,		batch.input_ids.shape						)
        self.assertEqual((2, 15)    ,		batch.attention_mask.shape						)
        __a       =    batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens    ,		lowerCamelCase						)
        self.assertEqual(lowerCamelCase    ,		batch.decoder_input_ids[0, 0]						)  # EOS
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens    ,		[EN_CODE]						)
        self.assertEqual(self.tokenizer.suffix_tokens    ,		[self.tokenizer.eos_token_id]						)


 def 						a__   ( self						):
        __a       =    self.tokenizer(self.src_text    ,		padding=lowerCamelCase    ,		truncation=lowerCamelCase    ,		max_length=3    ,		return_tensors="pt"						)
        __a       =    self.tokenizer(
            text_target=self.tgt_text    ,		padding=lowerCamelCase    ,		truncation=lowerCamelCase    ,		max_length=10    ,		return_tensors="pt"						)
        __a       =    targets["input_ids"]
        __a       =    shift_tokens_right(
            lowerCamelCase    ,		self.tokenizer.pad_token_id    ,		decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]    ,		)

        self.assertEqual(batch.input_ids.shape[1]    ,		3						)
        self.assertEqual(batch.decoder_input_ids.shape[1]    ,		10						)


 @require_torch
 def 						a__   ( self						):
        __a       =    self.tokenizer._build_translation_inputs(
            "A test"    ,		return_tensors="pt"    ,		src_lang="eng_Latn"    ,		tgt_lang="fra_Latn"						)

        self.assertEqual(
            nested_simplify(lowerCamelCase						)    ,		{
                # A, test, EOS, en_XX
                "input_ids": [[256047, 70, 7356, 2]],
                "attention_mask": [[1, 1, 1, 1]],
                # ar_AR
                "forced_bos_token_id": 256057,
            }    ,		)






 @require_torch
 def 						a__   ( self						):
        __a       =    True
        __a       =    self.tokenizer(
            "UN Chief says there is no military solution in Syria"    ,		src_lang="eng_Latn"    ,		tgt_lang="fra_Latn"						)
        self.assertEqual(
            inputs.input_ids    ,		[16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]						)

        __a       =    False
        __a       =    self.tokenizer(
            "UN Chief says there is no military solution in Syria"    ,		src_lang="eng_Latn"    ,		tgt_lang="fra_Latn"						)
        self.assertEqual(
            inputs.input_ids    ,		[256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]						)

"""simple docstring"""



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


SCREAMING_SNAKE_CASE__:Any									=			get_logger(__name__)


SCREAMING_SNAKE_CASE__:List[Any]									=			Path(__file__).parent / """model_card_template.md"""
SCREAMING_SNAKE_CASE__:str									=			uuida().hex
SCREAMING_SNAKE_CASE__:Optional[Any]									=			os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE__:List[Any]									=			os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE__:Optional[Any]									=			HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""



def 	_lowerCamelCase(	a = None				):
       __a       =    F"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
       if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
              return ua + "; telemetry/off"
       if is_torch_available():
              ua += F"; torch/{_torch_version}"
       if is_flax_available():
              ua += F"; jax/{_jax_version}"
              ua += F"; flax/{_flax_version}"
       if is_onnx_available():
              ua += F"; onnxruntime/{_onnxruntime_version}"
       # CI will set this value to True
       if os.environ.get("DIFFUSERS_IS_CI"   ,    ""				).upper() in ENV_VARS_TRUE_VALUES:
              ua += "; is_ci/true"
       if isinstance(a   ,    a				):
              ua += "; " + "; ".join(F"{k}/{v}" for k, v in user_agent.items()				)
       elif isinstance(a   ,    a				):
              ua += "; " + user_agent
       return ua



def 	_lowerCamelCase(	a   ,    a = None   ,    a = None				):
       if token is None:
              __a       =    HfFolder.get_token()
       if organization is None:
              __a       =    whoami(a				)["name"]
              return F"{username}/{model_id}"
       else:
              return F"{organization}/{model_id}"



def 	_lowerCamelCase(	a   ,    a				):
       if not is_jinja_available():
              raise ValueError(
                  "Modelcard rendering is based on Jinja templates."
                  " Please make sure to have `jinja` installed before using `create_model_card`."
                  " To install it, please run `pip install Jinja2`."				)

       if hasattr(a   ,    "local_rank"				) and args.local_rank not in [-1, 0]:
              return

       __a       =    args.hub_token if hasattr(a   ,    "hub_token"				) else None
       __a       =    get_full_repo_name(a   ,    token=a				)

       __a       =    ModelCard.from_template(
           card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
               language="en"   ,    license="apache-2.0"   ,    library_name="diffusers"   ,    tags=[]   ,    datasets=args.dataset_name   ,    metrics=[]   ,    )   ,    template_path=a   ,    model_name=a   ,    repo_name=a   ,    dataset_name=args.dataset_name if hasattr(a   ,    "dataset_name"				) else None   ,    learning_rate=args.learning_rate   ,    train_batch_size=args.train_batch_size   ,    eval_batch_size=args.eval_batch_size   ,    gradient_accumulation_steps=(
               args.gradient_accumulation_steps if hasattr(a   ,    "gradient_accumulation_steps"				) else None
           )   ,    adam_betaa=args.adam_betaa if hasattr(a   ,    "adam_beta1"				) else None   ,    adam_betaa=args.adam_betaa if hasattr(a   ,    "adam_beta2"				) else None   ,    adam_weight_decay=args.adam_weight_decay if hasattr(a   ,    "adam_weight_decay"				) else None   ,    adam_epsilon=args.adam_epsilon if hasattr(a   ,    "adam_epsilon"				) else None   ,    lr_scheduler=args.lr_scheduler if hasattr(a   ,    "lr_scheduler"				) else None   ,    lr_warmup_steps=args.lr_warmup_steps if hasattr(a   ,    "lr_warmup_steps"				) else None   ,    ema_inv_gamma=args.ema_inv_gamma if hasattr(a   ,    "ema_inv_gamma"				) else None   ,    ema_power=args.ema_power if hasattr(a   ,    "ema_power"				) else None   ,    ema_max_decay=args.ema_max_decay if hasattr(a   ,    "ema_max_decay"				) else None   ,    mixed_precision=args.mixed_precision   ,    )

       __a       =    os.path.join(args.output_dir   ,    "README.md"				)
       model_card.save(a				)



def 	_lowerCamelCase(	a   ,    a = None				):
       if resolved_file is None or commit_hash is not None:
              return commit_hash
       __a       =    str(Path(a				).as_posix()				)
       __a       =    re.search(R"snapshots/([^/]+)/"   ,    a				)
       if search is None:
              return None
       __a       =    search.groups()[0]
       return commit_hash if REGEX_COMMIT_HASH.match(a				) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
SCREAMING_SNAKE_CASE__:str									=			os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
SCREAMING_SNAKE_CASE__:List[Any]									=			os.path.join(hf_cache_home, """diffusers""")



def 	_lowerCamelCase(	a = None   ,    a = None				):
       if new_cache_dir is None:
              __a       =    DIFFUSERS_CACHE
       if old_cache_dir is None:
              __a       =    old_diffusers_cache

       __a       =    Path(a				).expanduser()
       __a       =    Path(a				).expanduser()
       for old_blob_path in old_cache_dir.glob("**/blobs/*"				):
              if old_blob_path.is_file() and not old_blob_path.is_symlink():
                     __a       =    new_cache_dir / old_blob_path.relative_to(a				)
                     new_blob_path.parent.mkdir(parents=a   ,    exist_ok=a				)
                     os.replace(a   ,    a				)
                     try:
                            os.symlink(a   ,    a				)
                     except OSError:
                            logger.warning(
                                "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."				)
       # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


SCREAMING_SNAKE_CASE__:Dict									=			os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
   SCREAMING_SNAKE_CASE__:Optional[int]									=			0
else:
   with open(cache_version_file) as f:
      try:
         SCREAMING_SNAKE_CASE__:List[str]									=			int(f.read())
      except ValueError:
         SCREAMING_SNAKE_CASE__:List[str]									=			0

if cache_version < 1:
   SCREAMING_SNAKE_CASE__:List[str]									=			os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
   if old_cache_is_not_empty:
      logger.warning(
          """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
          """existing cached models. This is a one-time operation, you can interrupt it or run it """
          """later by calling `diffusers.utils.hub_utils.move_cache()`."""
      )
      try:
         move_cache()
      except Exception as e:
         SCREAMING_SNAKE_CASE__:Optional[int]									=			"""\n""".join(traceback.format_tb(e.__traceback__))
         logger.error(
             F'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
             """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
             """message and we will do our best to help."""
         )

if cache_version < 1:
   try:
      os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
      with open(cache_version_file, """w""") as f:
         f.write("""1""")
   except Exception:
      logger.warning(
          F'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
          """the directory exists and can be written to."""
      )



def 	_lowerCamelCase(	a   ,    a = None				):
       if variant is not None:
              __a       =    weights_name.split("."				)
              __a       =    splits[:-1] + [variant] + splits[-1:]
              __a       =    ".".join(a				)

       return weights_name




def 	_lowerCamelCase(	a   ,    *,
    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a=None   ,    ):
       __a       =    str(a				)
       if os.path.isfile(a				):
              return pretrained_model_name_or_path
       elif os.path.isdir(a				):
              if os.path.isfile(os.path.join(a   ,    a				)				):
                     # Load from a PyTorch checkpoint
                     __a       =    os.path.join(a   ,    a				)
                     return model_file
              elif subfolder is not None and os.path.isfile(
                  os.path.join(a   ,    a   ,    a				)				):
                     __a       =    os.path.join(a   ,    a   ,    a				)
                     return model_file
              else:
                     raise EnvironmentError(
                         F"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."				)
       else:
              # 1. First check if deprecated way of loading from branches is used
              if (
                  revision in DEPRECATED_REVISION_ARGS
                  and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
                  and version.parse(version.parse(a				).base_version				) >= version.parse("0.20.0"				)
              ):
                     try:
                            __a       =    hf_hub_download(
                                a   ,    filename=_add_variant(a   ,    a				)   ,    cache_dir=a   ,    force_download=a   ,    proxies=a   ,    resume_download=a   ,    local_files_only=a   ,    use_auth_token=a   ,    user_agent=a   ,    subfolder=a   ,    revision=revision or commit_hash   ,    )
                            warnings.warn(
                                F"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."   ,    a   ,    )
                            return model_file
                     except:  # noqa: E722
                            warnings.warn(
                                F"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a   ,    a				)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a   ,    a				)}' so that the correct variant file can be added."   ,    a   ,    )
              try:
                     # 2. Load model file as usual
                     __a       =    hf_hub_download(
                         a   ,    filename=a   ,    cache_dir=a   ,    force_download=a   ,    proxies=a   ,    resume_download=a   ,    local_files_only=a   ,    use_auth_token=a   ,    user_agent=a   ,    subfolder=a   ,    revision=revision or commit_hash   ,    )
                     return model_file

              except RepositoryNotFoundError:
                     raise EnvironmentError(
                         F"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                         "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                         "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
                         "login`."				)
              except RevisionNotFoundError:
                     raise EnvironmentError(
                         F"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                         "this model name. Check the model page at "
                         F"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."				)
              except EntryNotFoundError:
                     raise EnvironmentError(
                         F"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."				)
              except HTTPError as err:
                     raise EnvironmentError(
                         F"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"				)
              except ValueError:
                     raise EnvironmentError(
                         F"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                         F" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                         F" directory containing a file named {weights_name} or"
                         " \nCheckout your internet connection or see how to run the library in"
                         " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."				)
              except EnvironmentError:
                     raise EnvironmentError(
                         F"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                         "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                         F"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                         F"containing a file named {weights_name}"				)
